
# Computations
import pandas as pd
import numpy as np
import calendar

# sklearn
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

# Pytorch
import torch
from torch.autograd import Variable
import torch.nn as nn 
import torchvision.transforms as transforms

# Visualisation libraries

## Progress Bar
import progressbar

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (17, 6)
mpl.rcParams['axes.labelsize'] = 14
mpl.rcParams['xtick.labelsize'] = 12
mpl.rcParams['ytick.labelsize'] = 12
mpl.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")


Data = pd.read_csv('Data/Bank_mod.csv')
display(Data.head().round(2))


Dataset_Subcategories = {}
Dataset_Subcategories['Bank Client Data'] = Data.iloc[:,:7].columns.tolist()
Dataset_Subcategories['Related with the Last Contact of the Current Campaign'] = Data.iloc[:,7:11].columns.tolist()
Dataset_Subcategories['Other Attributes'] = Data.iloc[:,11:15].columns.tolist()
Dataset_Subcategories['Social and Economic Context Attributes'] = Data.iloc[:,15:-1].columns.tolist()
Dataset_Subcategories['Output variable (Desired Target)'] = Data.iloc[:,-1:].columns.tolist()


def List_Print(Text, List):
    print(Back.BLACK + Fore.CYAN + Style.NORMAL + '%s:' % Text + Style.RESET_ALL + ' %s' % ', '.join(List))


def Data_Plot(Inp):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info.index.name = 'Features'
    data_info = data_info.reset_index(drop = False)
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type', text = 'Data Type',
                color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    fig.update_traces(texttemplate= 6*' ' + '%{label}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    fig.show()
    
def dtypes_group(Inp):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Features','Count'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Features'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
        Out.loc[Out.index == c, 'Count'] = len(Temp.loc[Temp['Data Type'] == c].index.tolist())
    Out.index.name = 'Data Type'
    Out = Out.reset_index(drop = False)
    Out['Data Type'] = Out['Data Type'].astype(str)
    return Out

Data_Plot(Data)
dType = dtypes_group(Data)


df = Data.copy()
Categorical_Variables = dType.loc[dType['Data Type'] == 'object'].values[0,1]
YN_Feat = []
for c in Categorical_Variables:
    s = set(df[c].unique().tolist())
    if s.issubset({'No', 'Yes', 'Unknown'}):
        YN_Feat.append(c)
del c, s
List_Print('Yes/No Features', YN_Feat)

# Converting:
Temp = {'Yes':1, 'No':0, 'Unknown':-1}
for c in YN_Feat:
    df[c] = df[c].replace(Temp).astype(int)    
del c
display(df[YN_Feat].head().style.hide_index())

## Adding these keys and values to a dictionary
CatVar_dict = {}
for c in YN_Feat:
    CatVar_dict[c] = Temp
    
#substracting YN Features from Categorical_Variables
Categorical_Variables = list(set(Categorical_Variables) - set(YN_Feat))
del YN_Feat, Temp

Yes/No Features: Housing, Loan, Default, Term Deposit Subscription


List_Print('Remaining categorical features', Categorical_Variables)

Remaining categorical features: Month, Contact, Poutcome, Marital, Day Of Week, Job, Education


Temp = {'Success':1, 'Failure':0, 'Nonexistent':-1}
df['Poutcome'] = df['Poutcome'].replace(Temp).astype(int)  
CatVar_dict['Poutcome'] = Temp
del Temp


Temp = {'Divorced':2, 'Married':1, 'Single':0, 'Unknown':-1}
df['Marital'] = df['Marital'].replace(Temp).astype(int)  
CatVar_dict['Marital'] = Temp


Temp = [x for x in calendar.day_name]
Temp0 = {}
for x in np.arange(len(Temp)):
    Temp0[Temp[x]] = x
del Temp
df['Day Of Week'] = df['Day Of Week'].replace(Temp0).astype(int)  
CatVar_dict['Day Of Week'] = Temp0


Temp = {'Telephone':0, 'Cellular':1}
df['Contact'] = df['Contact'].replace(Temp).astype(int)  
CatVar_dict['Contact'] = Temp


Temp = {'Unknown':-1, 'Unemployed':0, 'Student': 1, 'Housemaid':2, 
        'Retired':3, 'Blue-Collar':4, 'Self-Employed': 5, 'Services':6,
        'Technician':7, 'Admin.':8, 'Management':9, 'Entrepreneur':10 }
df['Job'] = df['Job'].replace(Temp).astype(int)  
CatVar_dict['Job'] = Temp


Temp = [x for x in calendar.month_name]
Temp = Temp[1:]
Temp0 = {}
for x in np.arange(len(Temp)):
    Temp0[Temp[x]] = x
del Temp

df['Month'] = df['Month'].replace(Temp0).astype(int)  
CatVar_dict['Month'] = Temp0


Temp = {'Unknown':-1, 'Illiterate':0, 'Basic.4Y':1, 'Basic.6Y':2, 'Basic.9Y':3, 'High.School':4,
        'Professional.Course':5,  'University.Degree':6}
df['Education'] = df['Education'].replace(Temp).astype(int)  
CatVar_dict['Education'] = Temp

Categorical_Variables = CatVar_dict
del CatVar_dict


df.loc[df['Pdays'] == 999, 'Pdays'] = -1


bins = pd.IntervalIndex.from_tuples([(14, 24), (24, 64),(64, 100)])
Temp = pd.cut(df['Age'], bins)
df['Age'] = Temp.astype(str).replace({'(14, 24]':0, '(24, 64]':1,'(64, 100]':2})


display(df.head())


Target = 'Term Deposit Subscription'
Labels = ['No', 'Yes']

Feat = 'Duration'
X = df[Feat].values.reshape(-1,1)
Test = np.arange(df[Feat].min(), df[Feat].max()).reshape(-1,1)
y = df[Target].values.reshape(-1,1)
logr = LogisticRegression(solver='newton-cg')
_ = logr.fit(X, y)
Pred_Prop = logr.predict_proba(Test)

fig, ax = plt.subplots(1, 1, figsize=(16,5))

# Right plot
_ = ax.scatter(X, y, color='HotPink', edgecolor = 'DeepPink')
_ = ax.plot(Test, Pred_Prop[:,1], color='MidnightBlue', lw = 1)
Temp = ax.get_xlim()
_ = ax.hlines(0, Temp[0], Temp[1], linestyles='dashed', lw=1)
_ = ax.hlines(1, Temp[0], Temp[1], linestyles='dashed', lw=1)
_ = ax.set_xlim(Temp)
_ = ax.set_xlabel('Last Contact Duration (in seconds)')
_ = ax.set_ylabel('Probability of %s' % Target)
_ = ax.set_title('Estimated Probability of %s using Logistic Regression' % Target )

del X, Test, y, logr, Pred_Prop, Temp


Feat = 'Pdays'
X = df[Feat].values.reshape(-1,1)
Test = np.arange(df[Feat].min(), df[Feat].max()).reshape(-1,1)
y = df[Target].values.reshape(-1,1)
logr = LogisticRegression(solver='newton-cg')
_ = logr.fit(X, y)
Pred_Prop = logr.predict_proba(Test)

fig, ax = plt.subplots(1, 1, figsize=(16,5))

# Right plot
_ = ax.scatter(X, y, color='Lime', edgecolor = 'LimeGreen', lw = 1)
_ = ax.plot(Test, Pred_Prop[:,1], color='MidnightBlue', lw = 1)
Temp = ax.get_xlim()
_ = ax.hlines(0, Temp[0], Temp[1], linestyles='dashed', lw=1)
_ = ax.hlines(1, Temp[0], Temp[1], linestyles='dashed', lw=1)
_ = ax.set_xlim(Temp)
_ = ax.set_xlabel('Days Passed Since the Previous Campaign Contact')
_ = ax.set_ylabel('Probability of %s' % Target)
_ = ax.set_title('Estimated Probability of %s using Logistic Regression' % Target )

del X, Test, y, logr, Pred_Prop, Temp


Fig, ax = plt.subplots(figsize=(17,16))
Temp = df.drop(columns = [Target]).var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T

_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette("OrRd", 20),
                  linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],
                  cbar_kws={'label': 'Feature Variance', "aspect":40, "shrink": .4, "orientation": "horizontal"})
labels = [x.replace(' ','\n').replace('Euribor\nthree','Euribor three').replace('\nof\n',' of\n')
          for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(labels)
_ = ax.set_yticklabels('')


# Scaling
Temp = df.drop(columns = Target).columns.tolist()
scaler = StandardScaler()
_ = scaler.fit(df[Temp])
df[Temp] = scaler.transform(df[Temp])

# Variance Plot
Fig, ax = plt.subplots(figsize=(17,16))

Temp = df.drop(columns = [Target]).var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T

_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette('Greens'),
                  linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],
                  cbar_kws={'label': 'Feature Variance', "aspect":40, "shrink": .4, "orientation": "horizontal"})
labels = [x.replace(' ','\n').replace('Euribor\nthree','Euribor three').replace('\nof\n',' of\n')
          for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(labels)
_ = ax.set_yticklabels('')


def Correlation_Plot (Df,Fig_Size):
    Correlation_Matrix = Df.corr().round(2)
    mask = np.zeros_like(Correlation_Matrix)
    mask[np.triu_indices_from(mask)] = True
    for i in range(len(mask)):
        mask[i,i]=0
    Fig, ax = plt.subplots(figsize=(Fig_Size,Fig_Size))
    sns.heatmap(Correlation_Matrix, ax=ax, mask=mask, annot=True, square=True, 
                cmap =sns.color_palette("Greens", n_colors=10), linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": .6})
    
Correlation_Plot (df, Fig_Size = 14)


Fig, ax = plt.subplots(figsize=(17,16))
Temp = df.corr().round(2)
Temp = Temp.loc[(Temp.index == Target)].drop(columns = Target).T.sort_values(by = Target).T
_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette("Greens", n_colors=10),
                linewidths = 0.8, vmin=0, vmax=1,
                annot_kws={"size": 12},
                cbar_kws={'label': Target + ' Correlation', "aspect":40, "shrink": .4, "orientation": "horizontal"})
labels = [x.replace(' ','\n').replace('Euribor\nthree','Euribor three').replace('\nof\n',' of\n')
          for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(labels)
_ = ax.set_yticklabels('')


df.to_csv (r'Data\Bank_mod_STD.csv', index = None, header=True)


def Header(Text, L = 100, C1 = Back.BLUE, C2 = Fore.BLUE):
    print(C1 + Fore.WHITE + Style.NORMAL + Text + Style.RESET_ALL + ' ' + C2 +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
def Line(L=100, C = Fore.BLUE): print(C + Style.NORMAL + L*'=' + Style.RESET_ALL)
    
def Search_List(Key, List): return [s for s in List if Key in s]

def Table1(Inp, Feat = Target):
    Out = Inp[Feat].value_counts().to_frame('Number of Instances').reset_index()
    Out = Out.rename(columns = {'index': Feat})
    Out['Percentage'] = np.round(100* Out['Number of Instances'].values /Out['Number of Instances'].sum(), 2)
    return Out

def Plot1(data, x= 'Number of Instances', Feat = Target, CL = ['FireBrick', 'SeaGreen']):
    fig = px.bar(data, x= x, y= ['',''], orientation='h', color = Feat, text = 'Percentage',
             color_discrete_sequence = CL, height = 180)
    fig.update_layout(plot_bgcolor= 'white', legend_orientation='h', legend=dict(x=0, y=1.7),
                      xaxis = dict(tickmode = 'array', tickvals = [0, Data.shape[0]], ticktext = ['','']))
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1.5, opacity=1)
    fig.update_traces(texttemplate='%{text:.2}%      ', textposition='inside')
    fig.update_xaxes(title_text=None, range=[0, Data.shape[0]])
    fig.update_yaxes(title_text=None)
    fig.show()
    
Plot1(data = Table1(Data, Feat = Target))


X = df.drop(columns = Target).values
y = df[Target].astype(float).values

Test_Size = 0.3

sss = StratifiedShuffleSplit(n_splits=1, test_size=Test_Size, random_state=42)
_ = sss.get_n_splits(X, y)
for train_index, test_index in sss.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
del sss

Header('Train Set')
Temp = Table1(Data, Feat = Target)
_, Temp['Number of Instances'] = np.unique(y_train, return_counts=True)
Temp['Percentage'] = np.round(100* Temp['Number of Instances'].values /Temp['Number of Instances'].sum(), 2)
display(Temp.style.format({'Percentage': "{:.2f}"}).hide_index())
Header('Test Set')
Temp = Table1(Data, Feat = Target)
_, Temp['Number of Instances'] = np.unique(y_test, return_counts=True)
Temp['Percentage'] = np.round(100* Temp['Number of Instances'].values /Temp['Number of Instances'].sum(), 2)
display(Temp.style.format({'Percentage': "{:.2f}"}).hide_index())
del Temp
Line()

Train Set ==========================================================================================

Test Set ===========================================================================================

====================================================================================================


if torch.cuda.is_available():
    X_train_tensor = Variable(torch.from_numpy(X_train).cuda())
    y_train_tensor = Variable(torch.from_numpy(y_train).type(torch.LongTensor).cuda())
    X_test_tensor = Variable(torch.from_numpy(X_test).cuda())
    y_test_tensor = Variable(torch.from_numpy(y_test).type(torch.LongTensor).cuda())
else:
    X_train_tensor = Variable(torch.from_numpy(X_train))
    y_train_tensor = Variable(torch.from_numpy(y_train).type(torch.LongTensor))
    X_test_tensor = Variable(torch.from_numpy(X_test))
    y_test_tensor = Variable(torch.from_numpy(y_test).type(torch.LongTensor))
    
Batch_size = 100
iteration_number = int(2e4)

epochs_number = int(iteration_number / (len(X_train) / Batch_size))

# Pytorch train and test sets
Train_set = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)
Test_set = torch.utils.data.TensorDataset(X_test_tensor, y_test_tensor)

# data loader
train_loader = torch.utils.data.DataLoader(Train_set, batch_size = Batch_size, shuffle = False)
test_loader = torch.utils.data.DataLoader(Train_set, batch_size = Batch_size, shuffle = False)


# Create MLP_Model
class MLP_Model(nn.Module):
    def __init__(self, input_Size, hidden_Size, output_Size):
        super(MLP_Model, self).__init__()
        # Linear function 1:
        self.fc1 = nn.Linear(input_Size, hidden_Size) 
        # Non-linearity 1
        self.relu1 = nn.ReLU()
              
        # Linear function 2:
        self.fc2 = nn.Linear(hidden_Size, output_Size)  
    
    def forward(self, x):
        # Linear function 1
        out = self.fc1(x)
        # Non-linearity 1
        out = self.relu1(out)
        
        # Linear function 2 (readout)
        out = self.fc2(out)
        return out


input_Size, output_Size = len(X[0]), len(np.unique(y))
hidden_Size = 128

# model
model = MLP_Model(input_Size, hidden_Size, output_Size)

# GPU
if torch.cuda.is_available():
    model.cuda()

# Cross Entropy Loss 
CEL= nn.CrossEntropyLoss()

# Optimizer 
learning_rate = 1e-2
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# Traning the Model
Count = 0
Loss_list = []
Iteration_list = []
Accuracy_list = []
MSE_list = []
MAE_list = []
Steps = 10

Progress_Bar = progressbar.ProgressBar(maxval= iteration_number + 200,
                                       widgets=[progressbar.Bar('=', '|', '|'),
                                                progressbar.Percentage()])
# print('---------------------------------------------------------')
for epoch in range(epochs_number):
    for i, (Xtr, ytr) in enumerate(train_loader):
        
        # Variables
        Xtr = Variable(Xtr.view(-1, X[0].shape[0]))
        ytr = Variable(ytr)
        
        # Set all gradients to zero
        optimizer.zero_grad()
        
        # Forward
        Out = model(Xtr.float())
        
        # loss
        loss = CEL(Out, ytr.long())
        
        # Backward (Calculating the gradients)
        loss.backward()
        
        # Update parameters
        optimizer.step()
        
        Count += 1
        
        del Xtr, ytr
        
        # Predictions
        if Count % Steps == 0:
            # Calculate Accuracy         
            Correct, Total = 0, 0
            # Predictions
            for Xts, yts in test_loader: 
                Xts = Variable(Xts.view(-1, X[0].shape[0]))
                
                # Forward
                Out = model(Xts.float())
                
                # The maximum value of Out
                Predicted = torch.max(Out.data, 1)[1]
                
                # Total number of yts
                Total += len(yts)
                
                # Total Correct predictions
                Correct += (Predicted == yts).sum()
            del Xts, yts
            # storing loss and iteration
            Loss_list.append(loss.data)
            Iteration_list.append(Count)
            Accuracy_list.append(Correct / float(Total))
            
        Progress_Bar.update(Count)

Progress_Bar.finish()

history = pd.DataFrame({'Iteration': np.array(Iteration_list),
                      'Loss': np.array([x.cpu().data.numpy() for x in Loss_list]),
                      'Accuracy': np.array([x.cpu().data.numpy() for x in Accuracy_list])})
del Loss_list, Iteration_list, Accuracy_list

|=========================================================================|100%


def Plot_history(history, Table_Rows = 25, yLim = 2):
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=[0.6, 0.4],
                        specs=[[{"type": "scatter"},{"type": "table"}]])
    # Left
    fig.add_trace(go.Scatter(x= history['Iteration'].values, y= history['Loss'].astype(float).values.round(4),
                             line=dict(color='OrangeRed', width= 1.5), name = 'Loss'), 1, 1)
    fig.add_trace(go.Scatter(x= history['Iteration'].values, y= history['Accuracy'].astype(float).values,
                             line=dict(color='MidnightBlue', width= 1.5),  name = 'Accuracy'), 1, 1)
    fig.update_layout(legend=dict(x=0, y=1.1, traceorder='reversed', font_size=12),
                  dragmode='select', plot_bgcolor= 'white', height=600, hovermode='closest',
                  legend_orientation='h')
    fig.update_xaxes(range=[history.Iteration.min(), history.Iteration.max()],
                     showgrid=True, gridwidth=1, gridcolor='Lightgray',
                     showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
    fig.update_yaxes(range=[0, yLim], showgrid=True, gridwidth=1, gridcolor='Lightgray',
                     showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
    # Right
    ind = np.linspace(0, history.shape[0], Table_Rows, endpoint = False).round(0).astype(int)
    ind = np.append(ind, history.Iteration.values[-1])
    history = history[history.index.isin(ind)]
    fig.add_trace(go.Table(header=dict(values = list(history.columns), line_color='darkslategray',
                                       fill_color='DimGray', align=['center','center'],
                                       font=dict(color='white', size=12), height=25), columnwidth = [0.4, 0.4, 0.4, 0.4],
                           cells=dict(values=[history.Iteration, history.Loss.astype(float).round(4).values,
                                          history.Accuracy.astype(float).round(4).values],
                                      line_color='darkslategray', fill=dict(color=['WhiteSmoke', 'white']),
                                      align=['center', 'center'], font_size=12,height=20)), 1, 2)
    fig.show()


Plot_history(history, Table_Rows = 18, yLim = 1)


def Confusion_Matrix(Model, FG = (12, 4), X_train_tensor = X_train_tensor, y_train = y_train,
                     X_test_tensor = X_test_tensor, y_test = y_test):
    
    font = FontProperties()
    font.set_weight('bold')
    ############# Train Set #############
    fig, ax = plt.subplots(1, 2, figsize=FG)
    _ = fig.suptitle('Train Set', fontproperties=font, fontsize = 16)
    
    # Predictions
    y_pred = model(X_train_tensor.float())
    y_pred = torch.max(y_pred.data, 1)[1]
    y_pred = y_pred.cpu().data.numpy()
    
    # confusion matrix
    CM = metrics.confusion_matrix(y_train, y_pred)
    CM_Train = CM.copy()
    _ = sns.heatmap(CM.round(2), annot=True, annot_kws={"size": 14}, cmap="Blues", ax = ax[0])
    _ = ax[0].set_title('Confusion Matrix')
    CM = CM.astype('float') / CM.sum(axis=1)[:, np.newaxis]
    _ = sns.heatmap(CM.round(2), annot=True, annot_kws={"size": 14}, cmap="Greens", ax = ax[1],
                   linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": 1})
    _ = ax[1].set_title('Normalized Confusion Matrix')
    
    for a in ax:
        _ = a.set_xlabel('Predicted labels')
        _ = a.set_ylabel('True labels')
        _ = a.xaxis.set_ticklabels(Labels)
        _ = a.yaxis.set_ticklabels(Labels)
    
    del CM
    ############# Test Set #############
    fig, ax = plt.subplots(1, 2, figsize=FG)
    _ = fig.suptitle('Test Set', fontproperties=font, fontsize = 16)
    font = FontProperties()
    font.set_weight('bold')
    
    # Predictions
    y_pred = model(X_test_tensor.float())
    y_pred = torch.max(y_pred.data, 1)[1]
    y_pred = y_pred.cpu().data.numpy()
    
    # confusion matrix
    CM = metrics.confusion_matrix(y_test, y_pred)
    CM_Test = CM.copy()
    _ = sns.heatmap(CM.round(2), annot=True, annot_kws={"size": 14}, cmap="Blues", ax = ax[0])
    _ = ax[0].set_title('Confusion Matrix')
    CM = CM.astype('float') / CM.sum(axis=1)[:, np.newaxis]
    _ = sns.heatmap(CM.round(2), annot=True, annot_kws={"size": 14}, cmap="Greens", ax = ax[1],
                   linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": 1})
    _ = ax[1].set_title('Normalized Confusion Matrix')
    
    for a in ax:
        _ = a.set_xlabel('Predicted labels')
        _ = a.set_ylabel('True labels')
        _ = a.xaxis.set_ticklabels(Labels)
        _ = a.yaxis.set_ticklabels(Labels)
    del CM
    return CM_Train, CM_Test


CM_Train, CM_Test = Confusion_Matrix(model)


Header('Train Set')
tn, fp, fn, tp = CM_Train.ravel()
Precision = tp/(tp+fp)
Recall = tp/(tp + fn)
TPR = tp/(tp +fn)
TNR = tn/(tn +fp)
BA = (TPR + TNR)/2
PPCR = (tp + fp)/(tp + fp + tn+ fn)

print('Precision (Train) = %.2f' % Precision)
print('Recall (Train) = %.2f' % Recall)
print('TPR (Train) = %.2f' % TPR)
print('TNR (Train) = %.2f' % TNR)
print('Balanced Accuracy (Train) = %.2f' % BA)
print('Predicted Positive Condition Rate (Train) = %.2f' % PPCR)

Header('Test Set')
tn, fp, fn, tp = CM_Test.ravel()
Precision = tp/(tp+fp)
Recall = tp/(tp + fn)
TPR = tp/(tp +fn)
TNR = tn/(tn +fp)
BA = (TPR + TNR)/2
PPCR = (tp + fp)/(tp + fp + tn+ fn)

print('Precision (Test) = %.2f' % Precision)
print('Recall (Test) = %.2f' % Recall)
print('TPR (Test) = %.2f' % TPR)
print('TNR (Test) = %.2f' % TNR)
print('Balanced Accuracy (Test) = %.2f' % BA)
print('Predicted Positive Condition Rate (Test) = %.2f' % PPCR)
del tn, fp, fn, tp, Precision, Recall, TPR, TNR, BA, PPCR
Line()

Train Set ==========================================================================================
Precision (Train) = 0.65
Recall (Train) = 0.50
TPR (Train) = 0.50
TNR (Train) = 0.97
Balanced Accuracy (Train) = 0.73
Predicted Positive Condition Rate (Train) = 0.09
Test Set ===========================================================================================
Precision (Test) = 0.64
Recall (Test) = 0.48
TPR (Test) = 0.48
TNR (Test) = 0.97
Balanced Accuracy (Test) = 0.72
Predicted Positive Condition Rate (Test) = 0.08
====================================================================================================


Sample = df.sample(frac = 0.1)
X_sample = Sample.drop(columns = [Target]).values
X_sample = scaler.transform(X_sample)

if torch.cuda.is_available():
    X_sample_tensor = Variable(torch.from_numpy(X_sample).cuda())
else:
    X_sample_tensor = Variable(torch.from_numpy(X_sample))

Labels = ['No', 'Yes']
y_pred = model(X_sample_tensor.float())
y_pred = np.asarray(y_pred.cpu().detach().numpy())
y_pred = pd.Series(y_pred.argmax(axis=1)).to_frame('Term Deposit Subscription (Predicted)').applymap(lambda x: Labels[0] if x ==0 else  Labels[1])
Predictions = pd.concat([Data, y_pred], axis = 1).dropna(subset = ['Term Deposit Subscription (Predicted)'])
display(Predictions)

Feature	Description
Contact	contact communication type (categorical: "cellular","telephone")
Month	last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
Day of week	last contact day of the week (categorical: "mon","tue","wed","thu","fri")
Duration	last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y="no"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Feature	Description
Employment Variation Rate	employment variation rate - quarterly indicator (numeric)
Consumer Price Index	consumer price index - monthly indicator (numeric)
Consumer Confidence Index	consumer confidence index - monthly indicator (numeric)
Euribor three Month Rate	euribor* 3 month rate - daily indicator (numeric)
Number of Employees	number of employees - quarterly indicator (numeric)

Interval	Age Category	Age Category Code
00-14 years	Children	0
15-24 years	Youth	1
25-64 years	Adults	2
65 years and over	Seniors	3

Bank Marketing Dataset¶

Dataset Information:¶

Loading the Dataset¶

Bank Client Data¶

Other Attributes¶

Output variable (Desired Target):¶

Preparing the Dataset¶

Categorical Variables¶

Yes/No Features¶

Poutcome¶

Marital¶

Day Of Week¶

Contact¶

Job¶

Month¶

Education¶

Pdays¶

Age Group and Age Category¶

Data Correlations¶

Saving to a CSV¶

Train and Test Sets¶

Modeling: PyTorch Multi-layer Perceptron (MLP) for Binary classification¶

Setting up Tensor Arrays¶

Modeling¶

Model Optimization Plot¶

Confusion Matrix, ROC, ...¶

Predictions¶

Conclutions¶

Refrences¶

	Age	Job	Marital	Education	Default	Housing	Loan	Contact	Month	Day Of Week	...	Campaign	Pdays	Poutcome	Employment Variation Rate	Consumer Price Index	Consumer Confidence Index	Euribor three Month Rate	Number of Employees	Term Deposit Subscription
0	56	Housemaid	Married	Basic.4Y	No	No	No	Telephone	May	Monday	...	1	999	Nonexistent	1.1	93.99	-36.4	4.86	5191.0	No
1	57	Services	Married	High.School	Unknown	No	No	Telephone	May	Monday	...	1	999	Nonexistent	1.1	93.99	-36.4	4.86	5191.0	No
2	37	Services	Married	High.School	No	Yes	No	Telephone	May	Monday	...	1	999	Nonexistent	1.1	93.99	-36.4	4.86	5191.0	No
3	40	Admin.	Married	Basic.6Y	No	No	No	Telephone	May	Monday	...	1	999	Nonexistent	1.1	93.99	-36.4	4.86	5191.0	No
4	56	Services	Married	High.School	No	No	Yes	Telephone	May	Monday	...	1	999	Nonexistent	1.1	93.99	-36.4	4.86	5191.0	No

Feature	Description
Age	numeric
Job	Type of Job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown")
Marital	marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed)
Education	(categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown")
Default	has credit in default? (categorical: "no","yes","unknown")
Housing	has housing loan? (categorical: "no","yes","unknown")
Loan	has personal loan? (categorical: "no","yes","unknown")

Feature	Description
Campaign	number of contacts performed during this campaign and for this client (numeric, includes last contact)
Pdays	number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
Previous	number of contacts performed before this campaign and for this client (numeric)
Poutcome	outcome of the previous marketing campaign (categorical: "failure","nonexistent","success")

Features	Age	Job	Marital	Education	Default	Housing	Loan	Month	...	Campaign	Pdays	Poutcome	Employment Variation Rate	Consumer Price Index	Consumer Confidence Index	Euribor three Month Rate	Number of Employees
0	1	2	1	1	0	0	0	4	...	1	-1	-1	1.1	93.994	-36.4	4.857	5191.0
1	1	6	1	4	-1	0	0	4	...	1	-1	-1	1.1	93.994	-36.4	4.857	5191.0
2	1	6	1	4	0	1	0	4	...	1	-1	-1	1.1	93.994	-36.4	4.857	5191.0
3	1	8	1	2	0	0	0	4	...	1	-1	-1	1.1	93.994	-36.4	4.857	5191.0
4	1	6	1	4	0	0	1	4	...	1	-1	-1	1.1	93.994	-36.4	4.857	5191.0

Term Deposit Subscription	Number of Instances	Percentage
No	25583	88.73
Yes	3248	11.27

Term Deposit Subscription	Number of Instances	Percentage
No	10965	88.74
Yes	1392	11.26

Bank Marketing Dataset¶

Dataset Information:¶

Loading the Dataset¶

Bank Client Data¶

Related with the Last Contact of the Current Campaign¶

Other Attributes¶

Social and Economic Context Attributes¶

Output variable (Desired Target):¶

Preparing the Dataset¶

Categorical Variables¶

Yes/No Features¶

Poutcome¶

Marital¶

Day Of Week¶

Contact¶

Job¶

Month¶

Education¶

Pdays¶

Age Group and Age Category¶

Data Correlations¶

Saving to a CSV¶

Train and Test Sets¶

Modeling: PyTorch Multi-layer Perceptron (MLP) for Binary classification¶

Setting up Tensor Arrays¶

Modeling¶

Model Optimization Plot¶

Confusion Matrix, ROC, ...¶

Predictions¶

Conclutions¶

Refrences¶